System and method for determining acceptability of proposed color solution using an artificial intelligence based tolerance model

ABSTRACT

system and method for determining if a proposed color solution, such as paint, pigments, or dye formulations, is acceptable, is provided. The inputs to the system are the color values of a proposed paint or other color formulation and differential color values. The system includes an input device for entering a proposed color solution and an artificial intelligence tolerance model coupled to the input device. The tolerance model produces an output signal for communicating whether the proposed color solution is acceptable. The artificial intelligence model may be embodied in a neural network. More specifically, the tolerance model may be a back propagation neural network.

FIELD OF THE INVENTION

[0001] The present invention relates generally to color matching, andmore particularly, to a method and system for assessing theacceptability of a color match using artificial intelligence.

BACKGROUND OF THE INVENTION

[0002] Products today are offered to consumers in a wide variety ofcolors. Consumer products may be colored by means of colorants or dye orpainted. Color matching is required in a variety of areas, includingtextiles, plastics, various synthetic materials, prosthetics, dentalapplications, and paint applications, due to the many variations incolor, and due to the wide variations in shades and hues of any givencolor and color variations in an article. The actual color produced in agiven article may vary due to a number of factors. For example, textilecolors vary according to fiber composition. Colorants for plastic varyaccording to the plastic composition. Painted articles vary in colordepending on any number of factors, such as paint composition,variations in the paint application process, including applicationmethod, film thickness, drying technique and number of layers. Animportant application for color matching is in the area of automotivecolor matching. Frequent uses for color matching in automotive paintoccur in matching the same color from different batches or matchingsimilar colors from different manufacturers. Additionally, there is arequirement for color matching refinish paint to an OEM (originalequipment manufacture) color when a vehicle body panels are damaged andrequire repainting.

[0003] A paint manufacturer supplies one or more paint formulations forthe original paint color to refinish paint shops. By supplying aplurality of formulations or variants for a particular color, the paintmanufacturer accounts for those factors that affect the actual color.Matching of dyes or colorants for other applications is also donethrough formulations for a particular color. Typically, the formulationsfor a particular color are distributed on paper, microfiche, and/orcompact disks (CD). A color tool, composed of swatches of the variantsfor each color may also be produced and delivered to each customer. Thecustomer must select a formulation most closely matching the existingcolor of the article. This is typically done visually, i.e., bycomparing swatches of paint or color to the part or in the case ofpaint, spraying a test piece with each formulation.

[0004] Different formulations are derived from actual data gathered byinspectors at various locations, e.g., the textile, plastic orautomobile manufacturer or vehicle distribution point. The inspectorstake color measurement readings from articles of a particular color.These readings are used to develop color solutions, i.e., differentformulations for the same color.

[0005] There are several disadvantages to the present method of colormatching. Conventional color laboratories that use human analysis todetermine color matching require significant numbers of people,equipment and materials for identifying pigments and locating a closematch from a database. In some cases, an existing formula may provide aclose match. In other cases, the formula must be adjusted, mixed,applied and compared to a standard. These steps are repeated until asuitably close match is found. In other cases, no match is found and aformula must be developed from scratch. Correction of the formularequires a highly skilled technician proficient in the interaction oflight with several different pigments.

[0006] Moreover, traditional computer software that assists a technicianhas several disadvantages. Traditional computer software has not provento be very effective on colors containing “effect pigments.” Thissoftware is typically based on a physical model of the interactionbetween illuminating light and the colorant or coating. These modelsinvolve complex physics and do not account for all aspects of thephenomena. A traditional approach is to use a model based on the work ofKubleka-Munk or modifications thereof. The model is difficult to employwith data obtained from multi-angle color measuring devices. Oneparticular difficulty is handling specular reflection that occurs nearthe gloss angle. Another deficiency of the Kubleka-Munk based models isthat only binary or ternary pigment mixtures are used to obtain theconstants of the model. Thus, the model may not properly account for thecomplexities of the multiple interactions prevalent in most paint orcolorant recipes.

[0007] The present invention is directed to solving or more of theproblems identified above.

SUMMARY OF THE INVENTION AND ADVANTAGES

[0008] Acceptable tolerances vary depending on the color. Tolerances areexpressed in differential color values, e.g., ΔL*, ΔC*, ΔH*. Thedifferential values will vary as a function of the color. Historically,these values have been determined manually, i.e., by visual evaluation.The tolerances for that formulation are determined as a function of allof the color measurement values that have been deemed acceptable(usually by visible methods).

[0009] In one aspect of the present invention, a system for determiningthe acceptability of a proposed color solution using an artificialintelligence tolerance model, is provided. The model is embodied in aneural network and, in particular, a feed-forward back propagationneural network. The color standard is expressed as color values(L*,C*,h*). The neural network is trained using the color values foreach formulation of each color and the differential color values fromall acceptable measurements.

[0010] When a proposed color solution has been chosen by a searchroutine, the color values of the solution from a composite solutiondatabase and color measurement data taken from the subject part form theinput to the neural network. The output of the neural network is whetheror not the color solution is acceptable. The neural network can also beused in other color difference measuring systems to expressacceptability of the measured color difference.

[0011] The neural network includes an input layer having nodes forreceiving input data related to color values of the standard anddifferences between the color values of the standard and the colorsolution. Weighted connections connect to the nodes of the input layerand have coefficients for weighting the input data. An output layerhaving nodes is either directly or indirectly connected to the weightedconnections. The output layer generates output data that is related tothe acceptability of the color match. The data of the input layer andthe data from the output layer are interrelated through the neuralnetwork's nonlinear relationship.

[0012] Neural networks have several advantages over conventionallogic-based expert systems or computational schemes. Neural networks areadaptive and provide parallel computing. Further, because neuralresponses are non-linear, a neural network is a non-linear device, whichis critical when applied to nonlinear problems. Moreover, systemsincorporating neural networks are fault tolerant because the informationis distributed throughout the network. Thus, system performance is notcatastrophically impaired if a processor experiences a fault.

[0013] Another aspect of the present invention provides a system and amethod for providing color solutions using an artificial intelligencetolerance model to a customer over a computer network. The systemincludes a first module located at a remote location. The first modulereceives a solution request from an operator. A second module is coupledto the first module via a computer network. The second module is locatedat a central location and includes a composite solution database, anartificial intelligence tolerance model and a search routine coupled tothe composite solution database. The second module is adapted to receivethe solution request from the first module. The search routine isadapted to search the composite solution database for a color code anddetermine a paint color solution from a plurality of color solutions asa function of the solution request. The artificial intelligencetolerance model is adapted to determine if the color solution chosen bythe search routine based on the color values of the solution input intothe first module is acceptable.

[0014] The method includes the steps of receiving a solution request andcolor values from an operator located at a remote location, deliveringthe solution request and color values from the remote location to acentral location over the computer network, and searching a compositesolution database for a color solution and determining a whether thecolor solution as a function of the solution request is acceptable.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other advantages of the present invention will be readilyappreciated as the same becomes better understood by reference to thefollowing detailed description when considered in connection with theaccompanying drawings wherein:

[0016]FIG. 1 is a block diagram of a system for determining theacceptability of a proposed color solution having an artificialintelligence model, according to an embodiment of the present invention;

[0017]FIG. 2 is a diagram depicting a neural network for use in theartificial intelligence model of FIG. 1, according to an embodiment ofthe present invention;

[0018]FIG. 3 is a block diagram depicting the training of the colortolerance neural network of FIG. 2, according to an embodiment of thepresent invention;

[0019]FIG. 4 is a block diagram of a color management and solutiondistribution system, according to an embodiment of the presentinvention;

[0020]FIG. 5 is a flow diagram of a color management and solutiondistribution method, according to an embodiment of the presentinvention; and

[0021]FIG. 6 is a block diagram of a color management and solutiondistribution method, according to another embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0022] Referring to the Figs., wherein like numerals indicate like orcorresponding parts throughout the several views, a system 100 andmethod 600 for determining if a proposed color solution, such as paint,pigments, or dye formulations, is acceptable, is provided.

[0023] For example, the proposed color solution may be a paintformulation to be used in the repair of an automobile body panel. Theinputs to the system are the color values (see below) of a proposedpaint formulation and differential color values. The differential colorvalues represent the differences between the color values of theproposed paint formulation and the actual color values of the part to berepaired.

[0024] With specific reference to FIG. 1, the system 100 includes aninput device 102 for entering a proposed color solution. Preferably, thesystem 100 is embodied in a computer program run on a general purposecomputer (not shown). The input device 102 may be embodied in a userinterface for inputting the proposed color solution, such as a keyboard.Furthermore, the input device 102 may be embodied in an element of acomputer system so as to receive the proposed color solution as inputfrom another element of the computer system, such as a computerdatabase, an electronic mail file or other suitable element of thecomputer system (see below).

[0025] The system 100 of the present invention further includes anartificial intelligence tolerance model 104 coupled to the input device102. The tolerance model 104 produces an output signal 106 forcommunicating whether the proposed color solution is acceptable. Theartificial intelligence model 104 may be embodied in a neural network.More specifically, the tolerance model 104 may be a back propagationneural network or any other suitable neural network. The output signal106 may be embodied in an acceptable/not acceptable format, anacceptance factor format or any other suitable format.

[0026] The proposed color solution includes color measurement data inthe form of color values. Color measurement data is an indication of theactual color of an object. Preferably, the color measurement data may bedetermined using a multi-angle or spherical geometry color measuringdevice, a spectrophotometer, digital camera or other suitable device.

[0027] Color values refer to color attributes used to quantify color.The color values may include color space values, reflectance values orother suitable color attributes. One example of color space values aredefined by L*a*b*, where L* represents luminous intensity, a* representsa red/green appearance, b* represents a yellow/blue appearance. Anotherexample of color space values are defined by L*, C*, h, where L*represents lightness, C* represents chroma, and h represents hue. Thecolor values (L*, a*, and b* or L*, C*, and h) at various angles areobtained using a color measurement device.

[0028] Referring to FIG. 2, an artificial neural network is generallyshown at 200. Artificial neural networks 200 are computing systems thatmodel vertebrate brain structure and processes. Artificial neuralnetwork techniques are a member of a group of methods which fall underthe umbrella of artificial intelligence. Artificial intelligence iscommonly associated with logic rule-based expert systems where the rulehierarchies used are reasoned from human knowledge. In contrast,artificial neural networks 200 are self-trained based on experienceacquired through data compilation and computation. Thus, artificialintelligence utilizing neural networks 200 is particularly useful inconjunction with complex systems or phenomena where the analysis iscomplicated, and deriving a model from human knowledge for use in aconventional expert system is a daunting task.

[0029] Although neural networks differ in geometry, activation functionand training mechanics, they are typically organized into at least threelayers. The first layer is an input layer 220 having one or more inputnodes 224, 226, 228. The second layer is an output layer 260 having oneor more output nodes 264, 266, 268. Each output node 264, 266, 268corresponds with an input node 224, 226, 228. Between the inner andouter layers, there are one or more hidden layers 240, each having oneor more hidden nodes 244, 246, 248 corresponding to an input node andoutput node pair 224,264, 226, 266, 228, 268. Each input variable isassociated with an input node 224, 226, 228 and each output variable isassociated with an output node 264, 266, 268. Within the neural network200, data flows in only one direction, such that each node 224, 226,228, 244, 246, 266, 268 only sends a signal to one or more nodes andreceives no feedback.

[0030] The enabling power of a neural network 200 is its connectivity,or the connections between the various nodes 224, 226, 228, 244, 246,266, 268. (A configuration technique modeled after the structure of thehuman brain.) Moreover, because the network is structured, or connected,in such a way as to provide parallel processing (where each node 224,226, 228, 244, 246, 266, 268 has connections with other nodes 224, 226,228, 244, 246, 266, 268), it is extremely efficient at acquiring andstoring experiential knowledge and, then recalling and using thatknowledge. More specifically, a node 224, 226, 228, 244, 246, 266, 268receives input values, processes them and provides an output. Theprocessing step includes summing the inputs, adding a bias value andsubmitting this total input to an activation function which limits themagnitude of the output. The connections between the various nodes 224,226, 228, 244, 246, 266, 268 are weighted. An output sent from one node224, 226, 228, 244, 246, 266, 268 to another is multiplied by theweighting factor associated between those two particular nodes 224, 226,228, 244, 246, 266, 268. The weighting factor represents the knowledgeof the system. The system continues to accumulate knowledge and adjustthe weighting factor in accordance with training and the furtheracquisition of knowledge by the network 200. Consequently, the output ofthe network 200 agrees with the experience of the network 200.

[0031] With particular reference to FIG. 1, the output of the tolerancemodel 104 may be communicated to a logic module 102 for transforming theoutput signal 106 into a desired format. The desired format of theoutput signal 106 may take the form of a single continuous variable, afuzzy variable set or any other suitable format.

[0032] A single continuous variable is a variable that may assume anyvalue between two endpoints. An example being the set of real numbersbetween 0 and 1.

[0033] A fuzzy variable set is the basis for a mathematical system offuzzy logic. “Fuzzy” refers to the uncertainty inherent in nearly alldata. Fuzzy logic may be used in artificial intelligence models,specifically neural networks, because there is a fuzziness in the outputof the neural network. Fuzzy logic is based on fuzzy variables. Inputsto a neural network may be provided for the fuzziness associated witheach network parameter. An output parameter depicting the fuzziness ofthe result could also be incorporated into the neural network. Theoutput parameter could range in value from 0 to 1, with a 1 indicatingno uncertainty in the result. For example, when gauging color matchquality, there may be uncertainty in the measurement of the color valuesand in the descriptive value of the goodness of the match. A fuzzyvariable set as an output signal from the neural network indicates thelevel of uncertainty and the quality level of the result. Thus, thequality and confidence of a color match may be expressed as 0.9, 0.8,where the quality is rated as very good at 0.9 and the confidence, orlevel of certainty, is quite high at 0.8.

[0034] With particular reference to FIG. 4, the neural network 104 ofthe subject invention is trained using the color values for eachformulation of each color and all acceptability results. There are twodifferent types of training (learning) for a neural network 104. Insupervised training (or external training), the network 104 is taught tomatch its output to external targets using data having input and outputpairs. In supervised training, the weighting factors are typicallymodified using a back-propagation method of learning where the outputerror is propagated back through the network 104. In unsupervisedtraining (or internal training), the input objects are mapped to anoutput space according to an internal criteria.

[0035] Referring to FIG. 3, in the preferred embodiment of the subjectinvention neural network 104 is a back propagation neural network 104.The training of the back propagation neural network 104 will now bediscussed. In a first process block 402 color values are provided to anartificial intelligence cluster model. In a second process block theartificial intelligence cluster model determines if the color solutionis acceptable. In a third process block 306, an output signal isproduced (see above).

[0036] In a fourth process block 308, acceptance ratings are input andtransformed into a desired format (fifth process block 310).

[0037] In a sixth process block 312, the transformed acceptance ratingsare input and compared to the output signal 106 of the neural network104. In a first decision block 314, if the output signal 106 is withinaccepted tolerance limits, no further action is taken. However, wherethe output signal 106 is outside the accepted tolerance limit, theplurality of weighted factors are adjusted based on the acceptancefactor output at the output signal 106 in a seventh process block 316.

[0038] With reference to FIG. 4, another embodiment of the presentinvention provides a computer system 400 for managing and providingcolor solutions, such as paint, pigments or dye formulations. The system400 includes a first module 402 located at a remote location 404, suchas a customer site. Preferably, the first module 402 is implemented on acomputer (not shown), such as a personal computer or wireless computingdevice. The first module 402 is adapted to be operated by a user oroperator 406, i.e., the customer. The operator 406 inputs a solutionrequest to the first module 402. The solution request includes a paintor color identifier (or color code) which identifies the color of asample or painted substrate 408, and color measurements from a colormeasurement device 410.

[0039] The color measurement device 410 is used to provide colormeasurements, i.e., an indication of the actual color of the sample 408.Preferably, the color measurement device 410 is a spectrophotometer suchas is available from X-Rite, Incorporated of Grandville, Minn. as modelno. MA58. Alternatively, the color measurement device 410 may be aspherical geometry color measuring device, a digital camera or othersuitable device.

[0040] The first module 402 is coupled to a second computer based module412 located at a central location 414, such as the paint, dye orcolorant manufacturer's facility. The first and second computer basedmodules 402, 412 are coupled across a computer network 416. In thepreferred embodiment, the computer network 416 is the internet.

[0041] The second module 412 receives the solution request from theoperator 406 via the first module 402 and the computer network 416. Thesecond module 412 includes a composite solution database 418, a searchengine or routine 420, and an artificial intelligence tolerance model422. The search routine 420 is adapted to search the composite solutiondatabase 418 and determine a paint color solution as a function of thesolution request. The artificial intelligence tolerance model 422 isadapted to determine if the color solution, chosen by the search routine420 based on the color values of the solution input into the firstmodule 402, is acceptable.

[0042] With reference to FIG. 5, a computer based method 500 forproviding color solutions to a customer will now be explained. In afirst control block 502, color values and, the solution request from theoperator 406 located at the remote location 404 is received. In a secondcontrol block 504, the solution request and color values are deliveredover the computer network 416 from the remote location 404 to thecentral location 404. In a third control block 506, the compositesolution database 418 is searched for a color solution and theacceptability of the color solution is determined.

[0043] With particular reference to FIG. 6, a system 600 for managingand providing color solutions using derived color tolerances isprovided. The system 600 includes three databases: the compositesolution database 418, a color measurement database 602, and a customerand solution usage database 604.

[0044] A customer interface 606 is implemented on the first module 402located at the remote location 604. The customer interface 606 allowsthe operator 406 to log on to the system, communicate with the system400,600, e.g., to request color solutions, to communicate color valuesand color measurement data, and to receive color solutions from thesystem 400,600. The customer interface 606 is graphical in nature, and,preferably, is accessed through a generic world wide web (WWW) browser,such as Microsoft™ Internet Explorer, available from Microsoft ofRedmond, Washington.

[0045] The customer interface 606 may be implemented in hyper textmarkup language (HTML), the JAVA language, and may include JavaScript.The system 600 also includes several processes: a solution creationprocess 608, a quality control process 610, a formula conversion process612, a variant determination process 614, and a derived toleranceprocess 616.

[0046] Referring to FIGS. I and 2, the artificial intelligence tolerancemodel 100 of the subject invention is embodied in a neural network 104.The tolerance model neural network 104 includes input data from theinput device 102 in the form of a proposed color solution having colorvalues. When a proposed color solution has been chosen by the searchroutine 420, the color values of the solution from the compositesolution database 418 form the input to the tolerance model neuralnetwork 406. The neural network 200 determines whether the proposedcolor solution is within the learned color tolerances and, thus, deemedacceptable.

[0047] Specifically, the subject invention neural network 200 includesan input layer 220 having a plurality of input nodes 224, 226, 228 forreceiving a color solution having color values. The subject inventionneural network 200 further includes an output layer 260 having aplurality of output nodes 264, 266, 268 for providing an acceptancefactor of the color solution wherein one of the plurality of input nodes224, 226, 228 corresponds with one of the plurality of output nodes 264,266, 268. The subject invention neural network 200 further includes ahidden layer 240 having a plurality of weighted factor nodes 244, 246,248 wherein one of the plurality of weighted factor nodes 244, 246, 248corresponds to one of the plurality of input nodes 224, 226, 228 and thecorresponding one of the plurality of output nodes 264, 266, 268. Theplurality of weighted factors non-linearly determine the contribution ofthe color values to the acceptance factor.

What is claimed is:
 1. A computer-based system for determining whether aproposed color solution is acceptable , comprising: an input device forreceiving the proposed color solution, the proposed color solutionincluding color values; and an artificial intelligence tolerance modelcoupled to the input device for producing an output signal forcommunicating whether the proposed color solution is acceptable.
 2. Acomputer-based system, as set forth in claim 1, wherein the artificialintelligence tolerance model is a neural network.
 3. A computer basedsystem, as set forth in claim 2, wherein the neural network is a backpropagation neural network.
 4. A computer-based system, as set forth inclaim 2, wherein the neural network includes an input layer having aplurality of input nodes for receiving the proposed color solution andan output layer having a plurality of output nodes and one of theplurality of input nodes.
 5. A computer-based system, as set forth inclaim 4, wherein the neural network includes a hidden layer having aplurality of weighted factors wherein one of the plurality of weightedfactors corresponds to one of the plurality of input nodes and acorresponding output node.
 6. A computer-based system, as set forth inclaim 5, wherein the plurality of weighted factors determine thecontribution of the color values to the output signal.
 7. Acomputer-based system, as set forth in claim 6, wherein the plurality ofweighted factors are adjusted as a function of the output signal.
 8. Acomputer-based system, as set forth in claim 7, wherein the outputsignal is an acceptance factor.
 9. A computer-based system, as set forthin claim 8, including an acceptance comparator for comparing theacceptance factor from the output layer to an acceptance standard andproviding feedback.
 10. A computer-based system, as set forth in claim9, wherein the plurality of weighted factors are adjusted as a functionof the feedback received by the input layer from the acceptancecomparator.
 11. A computer-based system, as set forth in claim 1,including a logic module for transforming the output nodes into adesired format.
 12. A computer-based system, as set forth in claim 10,wherein the desired format is a single continuous variable.
 13. Acomputer-based system, as set forth in claim 10, wherein the desiredformat is a fuzzy variable set.
 14. An artificial intelligence basedtolerance model for color solutions, comprising: an input layer having aplurality of input nodes for receiving a proposed color solution, theproposed color solution having color values; and an output layer havinga plurality of output nodes wherein one of the plurality of input nodescorresponds with one of the plurality of output nodes; wherein theoutput layer produces an output signal communicating whether the colorsolution is acceptable.
 15. An artificial intelligence model, as setforth in claim 14, wherein the model is a back propagation neuralnetwork.
 16. An artificial intelligence model, as set forth in claim 14,including a hidden layer having a plurality of weighted factors whereinone of the plurality of weighted factors corresponds to one of theplurality of input nodes and the corresponding one of the plurality ofoutput nodes.
 17. An artificial intelligence model, as set forth inclaim 16, wherein the plurality of weighted factors determine thecontribution of the color values to the output signal.
 18. An artificialintelligence model, as set forth in claim 17, wherein the plurality ofweighted factors are adjusted according to the output signal.
 19. Anartificial intelligence model, as set forth in claim 18, wherein theoutput signal is feedback at the input layer.
 20. An artificialintelligence system, as set forth in claim 19, wherein the plurality ofweighted factors are adjusted as a function of the feedback received bythe input layer.
 21. A computer system for providing a color solution toa customer, comprising: a first module located at a remote location andbeing adapted to receive a solution request from an operator; a secondmodule coupled to the first module and being located at a centrallocation, the second module including a composite solution database anda search routine coupled to the composite solution database and beingadapted to receive the solution request from the first module, thesearch routine being adapted to search the composite solution databaseand determine a proposed color solution as a function of the solutionrequest; and, an artificial intelligence model for determining theacceptability of the proposed color solution
 22. A computer system, asset forth in claim 21, wherein the artificial intelligence model is aneural network.
 23. A computer system, as set forth in claim 22, whereinthe artificial intelligence model is a back propagation neural network.24. A method for determining the acceptability of a proposed colorsolution using an artificial intelligence model, including the steps of:providing the proposed color solution to the model, the proposedsolution having color values; and producing an output signal indicativeof whether the proposed color solution is acceptable.
 25. A method, asset forth in claim 24, including the step of determining thecontribution of the color values to the output signal.
 26. A method, asset forth in claim 25, including the step of using a weighted factor todetermine the contribution of the color values to the output signal. 27.A method, as set forth in claim 26, including the step of comparing theoutput signal to an acceptance standard.
 28. A method, as set forth inclaim 27, including the step of training the artificial intelligencemodel for determining acceptability.
 29. A method, as set forth in claim28, wherein the artificial intelligence model is a neural network andthe method includes the step of providing feedback to the neural networkfrom the output signal for adjusting the weighted factor.
 30. A method,as set forth in claim 27, including the step of transforming the outputsignal into a desired format.
 31. A method, as set forth in claim 27,including the step of transforming the output signal into a singlecontinuous variable.
 32. A method, as set forth in claim 27, includingthe step of transforming the output signal into a fuzzy variable set.33. A method for determining the acceptability of a proposed colorsolution using a computer based model, the model being embodied in aneural network having an input layer and an output layer, including thesteps of: providing the proposed color solution to the neural network,the proposed color solution having color values; and producing an outputsignal indicative of whether the color solution is acceptable.
 34. Amethod, as set forth in claim 32 including the step of using a weightedfactor to determine the contribution of the color values to the outputsignal.
 35. A method, as set forth in claim 33 including the step ofadjusting the weighted factor according to the output signal.
 36. Amethod, as set forth in claim 34, including the step of providingfeedback from the output signal to the input layer.
 37. A method, as setforth in claim 35 including the step of adjusting the weighted factoraccording to the feedback received by the input layer.
 38. Acomputer-based method for providing a color solution to a customer overa computer network, including the steps of: receiving a solution requestfrom an operator located at a remote location; delivering the solutionrequest from the remote location to a central location over the computernetwork; searching a composite solution database and determining aproposed color solution as a function of the solution request; providingan artificial intelligence system for determining the acceptability ofthe proposed color solution and responsively producing an output signal.39. A method for training a neural network having an input layer, ahidden layer, and an output layer, the neural network being adapted todetermine the acceptability of a proposed color solution, comprising thesteps of: providing a plurality of acceptable color solutions to theinput layer, the acceptable color solutions having color values; using aweighted factor to the color values in the hidden layer to produce anoutput signal; providing the output signal to a comparator; providing anacceptance standard to the comparator to compare the acceptance standardand the output signal for producing an error value; comparing the errorvalue to an error limit to determine error variation; and providingerror feedback to the neural network corresponding to the errorvariation, wherein the weighted factor is adjusted according to theerror feedback.